Image process can be distinguished into image pre-process and image post-process. People can use an image extraction device to shoot scenery for acquiring the digital image data thereof. The acquired image data is generally called raw data. The raw data further will be processed for generating specific image effects. The procedure of using an image extraction device to shoot and acquire raw data is named image pre-process; the image processing procedure afterwards is called image post-process. The pre-processing procedures include the controls during image extraction, for example, auto focus and auto exposure. On the other hand, the general image post-processing procedures include noise reduction on raw data, white balancing, color interpolation, color calibration, gamma correction, color space conversion, edge enhancement, saturation enhancement, and false color suppression for obtaining images of superior quality.
In addition, with the prevalence of the digital optical devices such as digital still cameras and digital video cameras, people's requests for the image quality become demanding increasingly. Nonetheless, many factors in digital optical devices, including imaging defects caused by optical design, errors in lens processing, nonlinear characteristics and noises in image sensors, affect the imaging quality. In particular, defocus in image sensors tends to produce image blur. Unfortunately, the post-processing method described above does not estimate the blur degree of image for blur images. Thereby, the blur situation of defocused blur image cannot be improved. For solving the defocus problem, a point spread function (PSF) is developed by researchers in the image processing field.
Generally speaking, a digital optical device can use a point spread function for representing an optical path or an imaging system, where each object distance has a corresponding PSF for representing the optical characteristics of the optical path at the object distance. Besides, the image extracted by an image sensor is the convolution of the target image and the PSF. Current PSF is a Gaussian PSF according to the Gaussian function, which is used for representing the optical imaging system of a linear sensing device. In other words, the Gaussian PSF is a point spread function in an ideal condition. Nonetheless, a digital optical imaging system is an image extracting system built on nonlinear sensing devices. Thereby, imaging based on Gaussian PSF cannot represent the real target image exactly.
Consequently, even in a focused condition, the scenery in focus cannot be imaged on the image sensor perfectly. For the objects at other object distances, the imaging quality deteriorates significantly owing to defocus. In a defocused image, there usually exist only minor differences among nearby pixels, making the image intensity uniformly distributed among a plurality of pixels after convolution between the target image and the PSF. Thereby, the image extracted by the image sensor is blur with uniform intensity. Currently, the Gaussian PSF is adopted as the main function for modifying blur in defocused blur images. Because the Gaussian PSF, which can only modify blur in a linear system, cannot build an imaging model of an optical system using nonlinear sensing devices and current image modification technologies cannot estimate the blur degree of image in a blur image with certainty, current image modification technologies cannot eliminate the image blur generated by defocus effectively.
Accordingly, the present invention provides an estimation method for blur degree of image and an evaluation method for image quality, which match the pixel intensity distribution by using a synthesized image related to a non-uniform image and the input image. Thereby, the estimation for blur is given, which can be further applied to evaluation of image quality.